Which factors affect Airbnb Seattle Pricing ?¶

1. Introduction:¶

In this notebook we will use CRISP-DM methodology to explore and analyise Airbnb Seattle Dataset. Several methodologies and techniques will be used to demonstrate the findings including data wrangling and different data visualization techniques. We use CRISP-DM (Cross-Industry Standard Process for Data Mining) which is an industry-proven methodology for data mining which was proposed by IBM. CRISP-DM cosnsists of the following steps:¶

Business Understanding
Data Understanding
Data Preparation
Data Modeling
Evaluation
Deployment

2. Business Understanding:¶

Airbnb is the world leader in lodging industry with a turnover of USD 2.6B (2017). Airbnb is privately held and it serves the world from San Fransisco, California. Its crusial for such a service to understand why customers tend to select one host over the other, is it because of the area, the facilities and services, or other factors. Also, its. essential to understand how can to setup the cost and which features or factors are essential to dictate the cost.¶

In this report we will study and analyze Airbnb Seattle Dataset to answer the following questions:¶

Whom are the top 15 hosts in Seattle?
Which are the top 15 booked neighbourhoods ?
What are the top factors affecting the price?
What is the average of the accomodations based on the prvious factors?

To be able to answer the questions, we will be needing to get a dataset. We have used kaggle to get the Seattle Dataset¶

3. Data Understanding¶

In this step we will import the ncessary python libraries, loading the datasets, viewing, checking the shape of the datasets, data types, missing and null values and answer the basic questions¶

# Import the ncessary libraries

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.pyplot import plot
%matplotlib inline
import seaborn as sns
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)

# Import the data

listings=pd.read_csv('listings.csv')
listings_num=pd.read_csv('listings.csv')
reviews=pd.read_csv('reviews.csv')
calendar=pd.read_csv('calendar.csv')

# Checking the head and tail of listings table
listings.head()

listings.tail()

listings.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3818 entries, 0 to 3817
Data columns (total 92 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   id                                3818 non-null   int64  
 1   listing_url                       3818 non-null   object 
 2   scrape_id                         3818 non-null   int64  
 3   last_scraped                      3818 non-null   object 
 4   name                              3818 non-null   object 
 5   summary                           3641 non-null   object 
 6   space                             3249 non-null   object 
 7   description                       3818 non-null   object 
 8   experiences_offered               3818 non-null   object 
 9   neighborhood_overview             2786 non-null   object 
 10  notes                             2212 non-null   object 
 11  transit                           2884 non-null   object 
 12  thumbnail_url                     3498 non-null   object 
 13  medium_url                        3498 non-null   object 
 14  picture_url                       3818 non-null   object 
 15  xl_picture_url                    3498 non-null   object 
 16  host_id                           3818 non-null   int64  
 17  host_url                          3818 non-null   object 
 18  host_name                         3816 non-null   object 
 19  host_since                        3816 non-null   object 
 20  host_location                     3810 non-null   object 
 21  host_about                        2959 non-null   object 
 22  host_response_time                3295 non-null   object 
 23  host_response_rate                3295 non-null   object 
 24  host_acceptance_rate              3045 non-null   object 
 25  host_is_superhost                 3816 non-null   object 
 26  host_thumbnail_url                3816 non-null   object 
 27  host_picture_url                  3816 non-null   object 
 28  host_neighbourhood                3518 non-null   object 
 29  host_listings_count               3816 non-null   float64
 30  host_total_listings_count         3816 non-null   float64
 31  host_verifications                3818 non-null   object 
 32  host_has_profile_pic              3816 non-null   object 
 33  host_identity_verified            3816 non-null   object 
 34  street                            3818 non-null   object 
 35  neighbourhood                     3402 non-null   object 
 36  neighbourhood_cleansed            3818 non-null   object 
 37  neighbourhood_group_cleansed      3818 non-null   object 
 38  city                              3818 non-null   object 
 39  state                             3818 non-null   object 
 40  zipcode                           3811 non-null   object 
 41  market                            3818 non-null   object 
 42  smart_location                    3818 non-null   object 
 43  country_code                      3818 non-null   object 
 44  country                           3818 non-null   object 
 45  latitude                          3818 non-null   float64
 46  longitude                         3818 non-null   float64
 47  is_location_exact                 3818 non-null   object 
 48  property_type                     3817 non-null   object 
 49  room_type                         3818 non-null   object 
 50  accommodates                      3818 non-null   int64  
 51  bathrooms                         3802 non-null   float64
 52  bedrooms                          3812 non-null   float64
 53  beds                              3817 non-null   float64
 54  bed_type                          3818 non-null   object 
 55  amenities                         3818 non-null   object 
 56  square_feet                       97 non-null     float64
 57  price                             3818 non-null   object 
 58  weekly_price                      2009 non-null   object 
 59  monthly_price                     1517 non-null   object 
 60  security_deposit                  1866 non-null   object 
 61  cleaning_fee                      2788 non-null   object 
 62  guests_included                   3818 non-null   int64  
 63  extra_people                      3818 non-null   object 
 64  minimum_nights                    3818 non-null   int64  
 65  maximum_nights                    3818 non-null   int64  
 66  calendar_updated                  3818 non-null   object 
 67  has_availability                  3818 non-null   object 
 68  availability_30                   3818 non-null   int64  
 69  availability_60                   3818 non-null   int64  
 70  availability_90                   3818 non-null   int64  
 71  availability_365                  3818 non-null   int64  
 72  calendar_last_scraped             3818 non-null   object 
 73  number_of_reviews                 3818 non-null   int64  
 74  first_review                      3191 non-null   object 
 75  last_review                       3191 non-null   object 
 76  review_scores_rating              3171 non-null   float64
 77  review_scores_accuracy            3160 non-null   float64
 78  review_scores_cleanliness         3165 non-null   float64
 79  review_scores_checkin             3160 non-null   float64
 80  review_scores_communication       3167 non-null   float64
 81  review_scores_location            3163 non-null   float64
 82  review_scores_value               3162 non-null   float64
 83  requires_license                  3818 non-null   object 
 84  license                           0 non-null      float64
 85  jurisdiction_names                3818 non-null   object 
 86  instant_bookable                  3818 non-null   object 
 87  cancellation_policy               3818 non-null   object 
 88  require_guest_profile_picture     3818 non-null   object 
 89  require_guest_phone_verification  3818 non-null   object 
 90  calculated_host_listings_count    3818 non-null   int64  
 91  reviews_per_month                 3191 non-null   float64
dtypes: float64(17), int64(13), object(62)
memory usage: 2.7+ MB

For the listings dataset, we have 3,818 records/rows and 92 features/columns. With regards to the data types. We can see that there are 17 float columns, 13 int columns and 62 object columns. We can notice some empty null and missing values based on the number of records.¶

# Checking the head and tail of reviews table
reviews.head()

reviews.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 84849 entries, 0 to 84848
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   listing_id     84849 non-null  int64 
 1   id             84849 non-null  int64 
 2   date           84849 non-null  object
 3   reviewer_id    84849 non-null  int64 
 4   reviewer_name  84849 non-null  object
 5   comments       84831 non-null  object
dtypes: int64(3), object(3)
memory usage: 3.9+ MB

For the reviews dataset, there are 84,849 records/rows and 6 fatures/columns. There are 3 integer columns and 3 object columns. There are also missing values in the comments feature.¶

# Checking the head and tail of calendar table
calendar.head()

calendar.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1393570 entries, 0 to 1393569
Data columns (total 4 columns):
 #   Column      Non-Null Count    Dtype 
---  ------      --------------    ----- 
 0   listing_id  1393570 non-null  int64 
 1   date        1393570 non-null  object
 2   available   1393570 non-null  object
 3   price       934542 non-null   object
dtypes: int64(1), object(3)
memory usage: 42.5+ MB

For the calendar dataset, thre are 1,393,570 records/rows and 4 features/columns. There 1 integer and 3 object columns. There are missing values in the price column.¶

# Review the data with describtive statistics for the listings 
listings.describe()

reviews.describe()

calendar.describe()

From the above we can see that there are a lot of data inconsistency from data type prospective and will require cleaning and wrangling in terms of missing and nan values and replicated columns¶

4. Data Preparation:¶

In this step, the ncessary data is gathered to answer our questions. Replicated and uncessary columns are dropped. Missing and nan values are fixed. Categorial and other data types are handled.¶

We will use only listing table that reflect the required information on our questions¶

# Checking the % of the null values in every column of the listings dataset
(listings.isnull().sum()/len(listings)*100).sort_values(ascending=False)

license                             100.000000
square_feet                          97.459403
monthly_price                        60.267156
security_deposit                     51.126244
weekly_price                         47.380828
notes                                42.063908
neighborhood_overview                27.029859
cleaning_fee                         26.977475
transit                              24.463070
host_about                           22.498690
host_acceptance_rate                 20.246202
review_scores_accuracy               17.234154
review_scores_checkin                17.234154
review_scores_value                  17.181771
review_scores_location               17.155579
review_scores_cleanliness            17.103195
review_scores_communication          17.050812
review_scores_rating                 16.946045
reviews_per_month                    16.422211
first_review                         16.422211
last_review                          16.422211
space                                14.903091
host_response_time                   13.698271
host_response_rate                   13.698271
neighbourhood                        10.895757
xl_picture_url                        8.381351
thumbnail_url                         8.381351
medium_url                            8.381351
host_neighbourhood                    7.857517
summary                               4.635935
bathrooms                             0.419068
host_location                         0.209534
zipcode                               0.183342
bedrooms                              0.157150
host_identity_verified                0.052383
host_has_profile_pic                  0.052383
host_picture_url                      0.052383
host_since                            0.052383
host_total_listings_count             0.052383
host_listings_count                   0.052383
host_thumbnail_url                    0.052383
host_name                             0.052383
host_is_superhost                     0.052383
beds                                  0.026192
property_type                         0.026192
host_verifications                    0.000000
host_url                              0.000000
host_id                               0.000000
picture_url                           0.000000
experiences_offered                   0.000000
description                           0.000000
name                                  0.000000
last_scraped                          0.000000
scrape_id                             0.000000
listing_url                           0.000000
street                                0.000000
latitude                              0.000000
neighbourhood_cleansed                0.000000
calendar_last_scraped                 0.000000
calendar_updated                      0.000000
has_availability                      0.000000
availability_30                       0.000000
availability_60                       0.000000
availability_90                       0.000000
availability_365                      0.000000
number_of_reviews                     0.000000
minimum_nights                        0.000000
requires_license                      0.000000
jurisdiction_names                    0.000000
instant_bookable                      0.000000
cancellation_policy                   0.000000
require_guest_profile_picture         0.000000
require_guest_phone_verification      0.000000
maximum_nights                        0.000000
extra_people                          0.000000
neighbourhood_group_cleansed          0.000000
calculated_host_listings_count        0.000000
city                                  0.000000
state                                 0.000000
market                                0.000000
smart_location                        0.000000
country_code                          0.000000
country                               0.000000
longitude                             0.000000
guests_included                       0.000000
is_location_exact                     0.000000
room_type                             0.000000
accommodates                          0.000000
bed_type                              0.000000
amenities                             0.000000
price                                 0.000000
id                                    0.000000
dtype: float64

From the above we can see that the top 11 columns have > 20% missing values¶

# Checking the % of the null values in every column of the calendar dataset
(calendar.isnull().sum()/len(calendar)*100).sort_values(ascending=False)

price         32.938998
available      0.000000
date           0.000000
listing_id     0.000000
dtype: float64

# Checking the % of the null values in every column > 20%
# (listings.isnull().sum()/len(listings)*100).sort_values(ascending=False) >20

Dropping the columns with > 20% missing values for both calendar and listings datasets¶

# dropping the columns where missing values > 20%
listings = listings.drop(columns=['neighborhood_overview', 'cleaning_fee', 'transit', 'host_about', 'host_acceptance_rate', 'license', 'square_feet', 'monthly_price', 'security_deposit', 'weekly_price', 'notes'], axis =1)

# dropping the columns where missing values > 20%
calendar = calendar.drop(columns=['price'], axis =1)

# Checking the columns with missing values again in listings dataset
(listings.isnull().sum()/len(listings)*100).sort_values(ascending=False)

review_scores_accuracy              17.234154
review_scores_checkin               17.234154
review_scores_value                 17.181771
review_scores_location              17.155579
review_scores_cleanliness           17.103195
review_scores_communication         17.050812
review_scores_rating                16.946045
first_review                        16.422211
last_review                         16.422211
reviews_per_month                   16.422211
space                               14.903091
host_response_rate                  13.698271
host_response_time                  13.698271
neighbourhood                       10.895757
thumbnail_url                        8.381351
medium_url                           8.381351
xl_picture_url                       8.381351
host_neighbourhood                   7.857517
summary                              4.635935
bathrooms                            0.419068
host_location                        0.209534
zipcode                              0.183342
bedrooms                             0.157150
host_since                           0.052383
host_thumbnail_url                   0.052383
host_is_superhost                    0.052383
host_picture_url                     0.052383
host_name                            0.052383
host_listings_count                  0.052383
host_total_listings_count            0.052383
host_has_profile_pic                 0.052383
host_identity_verified               0.052383
property_type                        0.026192
beds                                 0.026192
street                               0.000000
host_verifications                   0.000000
host_id                              0.000000
host_url                             0.000000
neighbourhood_group_cleansed         0.000000
picture_url                          0.000000
experiences_offered                  0.000000
description                          0.000000
name                                 0.000000
last_scraped                         0.000000
scrape_id                            0.000000
listing_url                          0.000000
neighbourhood_cleansed               0.000000
latitude                             0.000000
city                                 0.000000
maximum_nights                       0.000000
require_guest_phone_verification     0.000000
require_guest_profile_picture        0.000000
cancellation_policy                  0.000000
instant_bookable                     0.000000
jurisdiction_names                   0.000000
requires_license                     0.000000
number_of_reviews                    0.000000
calendar_last_scraped                0.000000
availability_365                     0.000000
availability_90                      0.000000
availability_60                      0.000000
availability_30                      0.000000
has_availability                     0.000000
calendar_updated                     0.000000
minimum_nights                       0.000000
state                                0.000000
extra_people                         0.000000
guests_included                      0.000000
price                                0.000000
amenities                            0.000000
bed_type                             0.000000
accommodates                         0.000000
room_type                            0.000000
is_location_exact                    0.000000
longitude                            0.000000
calculated_host_listings_count       0.000000
country                              0.000000
country_code                         0.000000
smart_location                       0.000000
market                               0.000000
id                                   0.000000
dtype: float64

# Checking the columns with missing values again in calendar dataset
(calendar.isnull().sum()/len(calendar)*100).sort_values(ascending=False)

available     0.0
date          0.0
listing_id    0.0
dtype: float64

Checking the missing values¶

listings.isnull().sum().sum()

9984

calendar.isnull().sum().sum()

0

Replacing missing values with 0 in the listings and calendar datasets¶

listings.replace(np.nan, 0, inplace=True)

calendar.replace(np.nan, 0, inplace=True)

Checking the NaN values again¶

listings.isnull().sum()

id                                  0
listing_url                         0
scrape_id                           0
last_scraped                        0
name                                0
summary                             0
space                               0
description                         0
experiences_offered                 0
thumbnail_url                       0
medium_url                          0
picture_url                         0
xl_picture_url                      0
host_id                             0
host_url                            0
host_name                           0
host_since                          0
host_location                       0
host_response_time                  0
host_response_rate                  0
host_is_superhost                   0
host_thumbnail_url                  0
host_picture_url                    0
host_neighbourhood                  0
host_listings_count                 0
host_total_listings_count           0
host_verifications                  0
host_has_profile_pic                0
host_identity_verified              0
street                              0
neighbourhood                       0
neighbourhood_cleansed              0
neighbourhood_group_cleansed        0
city                                0
state                               0
zipcode                             0
market                              0
smart_location                      0
country_code                        0
country                             0
latitude                            0
longitude                           0
is_location_exact                   0
property_type                       0
room_type                           0
accommodates                        0
bathrooms                           0
bedrooms                            0
beds                                0
bed_type                            0
amenities                           0
price                               0
guests_included                     0
extra_people                        0
minimum_nights                      0
maximum_nights                      0
calendar_updated                    0
has_availability                    0
availability_30                     0
availability_60                     0
availability_90                     0
availability_365                    0
calendar_last_scraped               0
number_of_reviews                   0
first_review                        0
last_review                         0
review_scores_rating                0
review_scores_accuracy              0
review_scores_cleanliness           0
review_scores_checkin               0
review_scores_communication         0
review_scores_location              0
review_scores_value                 0
requires_license                    0
jurisdiction_names                  0
instant_bookable                    0
cancellation_policy                 0
require_guest_profile_picture       0
require_guest_phone_verification    0
calculated_host_listings_count      0
reviews_per_month                   0
dtype: int64

calendar.isnull().sum()

listing_id    0
date          0
available     0
dtype: int64

listings.head()

Dropping unessary or repliaceted columns¶

# dropping the uncessary and replicated columns.  
listings = listings.drop(columns=['host_name', 'calendar_updated', 'host_since', 'host_response_time', 'host_listings_count', 'neighbourhood_group_cleansed', 'zipcode', 'latitude', 'longitude', 'is_location_exact', 'has_availability', 'requires_license', 'country', 'city', 'state', 'street', 'market', 'smart_location', 'country_code', 'summary', 'description', 'space',  'jurisdiction_names', 'scrape_id', 'last_scraped', 'host_picture_url', 'host_verifications',  'listing_url', 'experiences_offered', 'thumbnail_url',  'medium_url', 'picture_url', 'host_url', 'host_thumbnail_url', 'xl_picture_url', 'calendar_last_scraped', 'neighbourhood', 'host_neighbourhood', 'first_review', 'last_review', 'name', 'host_location'], axis =1)

listings.head()

listings.info();

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3818 entries, 0 to 3817
Data columns (total 39 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   id                                3818 non-null   int64  
 1   host_id                           3818 non-null   int64  
 2   host_response_rate                3818 non-null   object 
 3   host_is_superhost                 3818 non-null   object 
 4   host_total_listings_count         3818 non-null   float64
 5   host_has_profile_pic              3818 non-null   object 
 6   host_identity_verified            3818 non-null   object 
 7   neighbourhood_cleansed            3818 non-null   object 
 8   property_type                     3818 non-null   object 
 9   room_type                         3818 non-null   object 
 10  accommodates                      3818 non-null   int64  
 11  bathrooms                         3818 non-null   float64
 12  bedrooms                          3818 non-null   float64
 13  beds                              3818 non-null   float64
 14  bed_type                          3818 non-null   object 
 15  amenities                         3818 non-null   object 
 16  price                             3818 non-null   object 
 17  guests_included                   3818 non-null   int64  
 18  extra_people                      3818 non-null   object 
 19  minimum_nights                    3818 non-null   int64  
 20  maximum_nights                    3818 non-null   int64  
 21  availability_30                   3818 non-null   int64  
 22  availability_60                   3818 non-null   int64  
 23  availability_90                   3818 non-null   int64  
 24  availability_365                  3818 non-null   int64  
 25  number_of_reviews                 3818 non-null   int64  
 26  review_scores_rating              3818 non-null   float64
 27  review_scores_accuracy            3818 non-null   float64
 28  review_scores_cleanliness         3818 non-null   float64
 29  review_scores_checkin             3818 non-null   float64
 30  review_scores_communication       3818 non-null   float64
 31  review_scores_location            3818 non-null   float64
 32  review_scores_value               3818 non-null   float64
 33  instant_bookable                  3818 non-null   object 
 34  cancellation_policy               3818 non-null   object 
 35  require_guest_profile_picture     3818 non-null   object 
 36  require_guest_phone_verification  3818 non-null   object 
 37  calculated_host_listings_count    3818 non-null   int64  
 38  reviews_per_month                 3818 non-null   float64
dtypes: float64(12), int64(12), object(15)
memory usage: 1.1+ MB

We need to to clean the prices columns by taking out the $ symbol and converting the column from object to float¶

listings['price'] = listings.price.str.strip('$')

listings['extra_people'] = listings.extra_people.str.strip('$')

listings['price'] = pd.to_numeric(listings['price'],errors='coerce')

listings['extra_people'] = pd.to_numeric(listings['extra_people'],errors='coerce')

converting host_since from object to datetime datatype¶

calendar['date'] = pd.to_datetime(calendar['date'])

We need to to clean the columns with % by taking out the % symbol and converting the column from object to float¶

listings['host_response_rate'] = listings.host_response_rate.str.strip('%')

listings['host_response_rate'] = pd.to_numeric(listings['host_response_rate'],errors='coerce')

listings.dtypes

id                                    int64
host_id                               int64
host_response_rate                  float64
host_is_superhost                    object
host_total_listings_count           float64
host_has_profile_pic                 object
host_identity_verified               object
neighbourhood_cleansed               object
property_type                        object
room_type                            object
accommodates                          int64
bathrooms                           float64
bedrooms                            float64
beds                                float64
bed_type                             object
amenities                            object
price                               float64
guests_included                       int64
extra_people                        float64
minimum_nights                        int64
maximum_nights                        int64
availability_30                       int64
availability_60                       int64
availability_90                       int64
availability_365                      int64
number_of_reviews                     int64
review_scores_rating                float64
review_scores_accuracy              float64
review_scores_cleanliness           float64
review_scores_checkin               float64
review_scores_communication         float64
review_scores_location              float64
review_scores_value                 float64
instant_bookable                     object
cancellation_policy                  object
require_guest_profile_picture        object
require_guest_phone_verification     object
calculated_host_listings_count        int64
reviews_per_month                   float64
dtype: object

Converting the categorial varilables to numeric¶

listings_num = listings.copy()

listings_num['host_identity_verified'] = listings_num['host_identity_verified'].astype('category').cat.codes

listings_num['host_is_superhost'] = listings_num['host_is_superhost'].astype('category').cat.codes

listings_num['host_has_profile_pic'] = listings_num['host_has_profile_pic'].astype('category').cat.codes

listings_num['property_type'] = listings_num['property_type'].astype('category').cat.codes

listings_num['room_type'] = listings_num['room_type'].astype('category').cat.codes

listings_num['bed_type'] = listings_num['bed_type'].astype('category').cat.codes

listings_num['amenities'] = listings_num['amenities'].astype('category').cat.codes

listings_num['instant_bookable'] = listings_num['instant_bookable'].astype('category').cat.codes

listings_num['cancellation_policy'] = listings_num['cancellation_policy'].astype('category').cat.codes

listings_num['require_guest_profile_picture'] = listings_num['require_guest_profile_picture'].astype('category').cat.codes

listings_num['require_guest_phone_verification'] = listings_num['require_guest_phone_verification'].astype('category').cat.codes

listings_num['id'] = listings_num['id'].astype('category').cat.codes

listings_num['host_id'] = listings_num['host_id'].astype('category').cat.codes

listings_num['neighbourhood_cleansed'] = listings_num['neighbourhood_cleansed'].astype('category').cat.codes

listings_num.head()

Finding out if there is host name duplicates

5. Data Modeling:¶

# listings_dummy.head()

# listings_dummy = pd.get_dummies(listings, drop_first=True)

# dups_shape = listings.pivot_table(index=['host_id'], aggfunc='size')

# dups_shape.index

listings_num.corr()

tophost=listings.host_id.value_counts().head(15)
tophost

8534462     46
4962900     39
1243056     37
430709      36
3074414     34
74305       33
26967583    21
7354306     18
42537846    16
1623580     12
658155      12
2911360     11
862329      11
754810      10
31148752    10
Name: host_id, dtype: int64

p1=tophost.plot(kind='bar')
p1.set_title('Hosts with the most listings')
p1.set_ylabel('Count of listings')
p1.set_xlabel('Host IDs')
p1.set_xticklabels(p1.get_xticklabels(), rotation=90);

topprice=listings.price.max()
topprice

999.0

top_neighbourhood_cleansed=listings.neighbourhood_cleansed.value_counts().head(15)
top_neighbourhood_cleansed

Broadway                     397
Belltown                     234
Wallingford                  167
Fremont                      158
Minor                        135
University District          122
Stevens                      119
First Hill                   108
Central Business District    103
Lower Queen Anne              94
Greenwood                     89
East Queen Anne               82
North Beacon Hill             78
Phinney Ridge                 73
Adams                         70
Name: neighbourhood_cleansed, dtype: int64

p2=top_neighbourhood_cleansed.plot(kind='bar')
p2.set_title('Neighbourhoods with the most Stays')
p2.set_ylabel('Count of Stays')
p2.set_xlabel('Neighbourhood Name')
p2.set_xticklabels(p2.get_xticklabels(), rotation=90);

listings_num.columns

Index(['id', 'host_id', 'host_response_rate', 'host_is_superhost',
       'host_total_listings_count', 'host_has_profile_pic',
       'host_identity_verified', 'neighbourhood_cleansed', 'property_type',
       'room_type', 'accommodates', 'bathrooms', 'bedrooms', 'beds',
       'bed_type', 'amenities', 'price', 'guests_included', 'extra_people',
       'minimum_nights', 'maximum_nights', 'availability_30',
       'availability_60', 'availability_90', 'availability_365',
       'number_of_reviews', 'review_scores_rating', 'review_scores_accuracy',
       'review_scores_cleanliness', 'review_scores_checkin',
       'review_scores_communication', 'review_scores_location',
       'review_scores_value', 'instant_bookable', 'cancellation_policy',
       'require_guest_profile_picture', 'require_guest_phone_verification',
       'calculated_host_listings_count', 'reviews_per_month'],
      dtype='object')

listings_num.head()

Using the numerical values of the categories to check which. factors are affecting the price¶

col = ['host_response_rate', 'host_is_superhost',
       'host_total_listings_count', 'host_has_profile_pic',
       'host_identity_verified', 'neighbourhood_cleansed', 'property_type',
       'room_type', 'accommodates', 'bathrooms', 'bedrooms', 'beds',
       'bed_type', 'amenities', 'price', 'guests_included', 'extra_people',
       'minimum_nights', 'maximum_nights', 'availability_30',
       'availability_60', 'availability_90', 'availability_365',
       'number_of_reviews', 'review_scores_rating', 'review_scores_accuracy',
       'review_scores_cleanliness', 'review_scores_checkin',
       'review_scores_communication', 'review_scores_location',
       'review_scores_value', 'instant_bookable', 'cancellation_policy',
       'require_guest_profile_picture', 'require_guest_phone_verification',
       'calculated_host_listings_count', 'reviews_per_month']
sns.set(style="whitegrid", color_codes=True)
sns.pairplot(listings_num.loc[(listings_num.price > 0)][col].dropna())
plt.show();

corr = listings_num.loc[(listings_num.price > 0)][col].dropna().corr()
plt.figure(figsize = (16,16))
sns.set(font_scale=1)
sns.heatmap(corr, cbar = True, annot=True, fmt = '.1f', xticklabels=col, yticklabels=col)
plt.show();

# line graphs linear regression plot price vs accommodates
sns.lmplot(x='accommodates', y="price", data=listings)

<seaborn.axisgrid.FacetGrid at 0x1a3f5ae1d0>

# line graphs linear regression plot price vs bedrooms
sns.lmplot(x='bedrooms', y="price", data=listings, hue='accommodates')

<seaborn.axisgrid.FacetGrid at 0x1a399fb9d0>

# line graphs linear regression plot price vs beds
sns.lmplot(x='beds', y="price", data=listings)

<seaborn.axisgrid.FacetGrid at 0x1a3a6f9c10>

# using subplot with pltmatlib 1 x 2
fig, (ax1, ax2) = plt.subplots(1, 2)
sns.distplot(listings.price, ax=ax1)
sns.boxplot(x='accommodates', y='price', data=listings, ax=ax2)

<matplotlib.axes._subplots.AxesSubplot at 0x1a6849cd50>

# using subplot with pltmatlib 1 x 2
fig, (ax1, ax2) = plt.subplots(1, 2)
sns.distplot(listings.price, ax=ax1)
sns.boxplot(x='beds', y='price', data=listings, ax=ax2)

<matplotlib.axes._subplots.AxesSubplot at 0x1a67fc9c50>

# disttibution and histogram
sns.distplot(listings.price)

<matplotlib.axes._subplots.AxesSubplot at 0x1a6771e250>

6. Conclusion and Observation¶

We found out that the price mainly affected the following factors using the correlation hotmap

Number of poeple the listing can accomodates
Number of bedrooms
NUmber of beds

We noticed that the price increase is propotinal to the number of people it accomodates and/or number of bedrooms and beds.

	id	listing_url	scrape_id	last_scraped	name	summary	space	description	experiences_offered	neighborhood_overview	notes	transit	thumbnail_url	medium_url	picture_url	xl_picture_url	host_id	host_url	host_name	host_since	host_location	host_about	host_response_time	host_response_rate	host_acceptance_rate	host_is_superhost	host_thumbnail_url	host_picture_url	host_neighbourhood	host_listings_count	host_total_listings_count	host_verifications	host_has_profile_pic	host_identity_verified	street	neighbourhood	neighbourhood_cleansed	neighbourhood_group_cleansed	city	state	zipcode	market	smart_location	country_code	country	latitude	longitude	is_location_exact	property_type	room_type	accommodates	bathrooms	bedrooms	beds	bed_type	amenities	square_feet	price	weekly_price	monthly_price	security_deposit	cleaning_fee	guests_included	extra_people	minimum_nights	maximum_nights	calendar_updated	has_availability	availability_30	availability_60	availability_90	availability_365	calendar_last_scraped	number_of_reviews	first_review	last_review	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	requires_license	license	jurisdiction_names	instant_bookable	cancellation_policy	require_guest_profile_picture	require_guest_phone_verification	calculated_host_listings_count	reviews_per_month
0	241032	https://www.airbnb.com/rooms/241032	20160104002432	2016-01-04	Stylish Queen Anne Apartment	NaN	Make your self at home in this charming one-be...	Make your self at home in this charming one-be...	none	NaN	NaN	NaN	NaN	NaN	https://a1.muscache.com/ac/pictures/67560560/c...	NaN	956883	https://www.airbnb.com/users/show/956883	Maija	2011-08-11	Seattle, Washington, United States	I am an artist, interior designer, and run a s...	within a few hours	96%	100%	f	https://a0.muscache.com/ac/users/956883/profil...	https://a0.muscache.com/ac/users/956883/profil...	Queen Anne	3.0	3.0	['email', 'phone', 'reviews', 'kba']	t	t	Gilman Dr W, Seattle, WA 98119, United States	Queen Anne	West Queen Anne	Queen Anne	Seattle	WA	98119	Seattle	Seattle, WA	US	United States	47.636289	-122.371025	t	Apartment	Entire home/apt	4	1.0	1.0	1.0	Real Bed	{TV,"Cable TV",Internet,"Wireless Internet","A...	NaN	$85.00	NaN	NaN	NaN	NaN	2	$5.00	1	365	4 weeks ago	t	14	41	71	346	2016-01-04	207	2011-11-01	2016-01-02	95.0	10.0	10.0	10.0	10.0	9.0	10.0	f	NaN	WASHINGTON	f	moderate	f	f	2	4.07
1	953595	https://www.airbnb.com/rooms/953595	20160104002432	2016-01-04	Bright & Airy Queen Anne Apartment	Chemically sensitive? We've removed the irrita...	Beautiful, hypoallergenic apartment in an extr...	Chemically sensitive? We've removed the irrita...	none	Queen Anne is a wonderful, truly functional vi...	What's up with the free pillows? Our home was...	Convenient bus stops are just down the block, ...	https://a0.muscache.com/ac/pictures/14409893/f...	https://a0.muscache.com/im/pictures/14409893/f...	https://a0.muscache.com/ac/pictures/14409893/f...	https://a0.muscache.com/ac/pictures/14409893/f...	5177328	https://www.airbnb.com/users/show/5177328	Andrea	2013-02-21	Seattle, Washington, United States	Living east coast/left coast/overseas. Time i...	within an hour	98%	100%	t	https://a0.muscache.com/ac/users/5177328/profi...	https://a0.muscache.com/ac/users/5177328/profi...	Queen Anne	6.0	6.0	['email', 'phone', 'facebook', 'linkedin', 're...	t	t	7th Avenue West, Seattle, WA 98119, United States	Queen Anne	West Queen Anne	Queen Anne	Seattle	WA	98119	Seattle	Seattle, WA	US	United States	47.639123	-122.365666	t	Apartment	Entire home/apt	4	1.0	1.0	1.0	Real Bed	{TV,Internet,"Wireless Internet",Kitchen,"Free...	NaN	$150.00	$1,000.00	$3,000.00	$100.00	$40.00	1	$0.00	2	90	today	t	13	13	16	291	2016-01-04	43	2013-08-19	2015-12-29	96.0	10.0	10.0	10.0	10.0	10.0	10.0	f	NaN	WASHINGTON	f	strict	t	t	6	1.48
2	3308979	https://www.airbnb.com/rooms/3308979	20160104002432	2016-01-04	New Modern House-Amazing water view	New modern house built in 2013. Spectacular s...	Our house is modern, light and fresh with a wa...	New modern house built in 2013. Spectacular s...	none	Upper Queen Anne is a charming neighborhood fu...	Our house is located just 5 short blocks to To...	A bus stop is just 2 blocks away. Easy bus a...	NaN	NaN	https://a2.muscache.com/ac/pictures/b4324e0f-a...	NaN	16708587	https://www.airbnb.com/users/show/16708587	Jill	2014-06-12	Seattle, Washington, United States	i love living in Seattle. i grew up in the mi...	within a few hours	67%	100%	f	https://a1.muscache.com/ac/users/16708587/prof...	https://a1.muscache.com/ac/users/16708587/prof...	Queen Anne	2.0	2.0	['email', 'phone', 'google', 'reviews', 'jumio']	t	t	West Lee Street, Seattle, WA 98119, United States	Queen Anne	West Queen Anne	Queen Anne	Seattle	WA	98119	Seattle	Seattle, WA	US	United States	47.629724	-122.369483	t	House	Entire home/apt	11	4.5	5.0	7.0	Real Bed	{TV,"Cable TV",Internet,"Wireless Internet","A...	NaN	$975.00	NaN	NaN	$1,000.00	$300.00	10	$25.00	4	30	5 weeks ago	t	1	6	17	220	2016-01-04	20	2014-07-30	2015-09-03	97.0	10.0	10.0	10.0	10.0	10.0	10.0	f	NaN	WASHINGTON	f	strict	f	f	2	1.15
3	7421966	https://www.airbnb.com/rooms/7421966	20160104002432	2016-01-04	Queen Anne Chateau	A charming apartment that sits atop Queen Anne...	NaN	A charming apartment that sits atop Queen Anne...	none	NaN	NaN	NaN	NaN	NaN	https://a0.muscache.com/ac/pictures/94146944/6...	NaN	9851441	https://www.airbnb.com/users/show/9851441	Emily	2013-11-06	Seattle, Washington, United States	NaN	NaN	NaN	NaN	f	https://a2.muscache.com/ac/users/9851441/profi...	https://a2.muscache.com/ac/users/9851441/profi...	Queen Anne	1.0	1.0	['email', 'phone', 'facebook', 'reviews', 'jum...	t	t	8th Avenue West, Seattle, WA 98119, United States	Queen Anne	West Queen Anne	Queen Anne	Seattle	WA	98119	Seattle	Seattle, WA	US	United States	47.638473	-122.369279	t	Apartment	Entire home/apt	3	1.0	0.0	2.0	Real Bed	{Internet,"Wireless Internet",Kitchen,"Indoor ...	NaN	$100.00	$650.00	$2,300.00	NaN	NaN	1	$0.00	1	1125	6 months ago	t	0	0	0	143	2016-01-04	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	f	NaN	WASHINGTON	f	flexible	f	f	1	NaN
4	278830	https://www.airbnb.com/rooms/278830	20160104002432	2016-01-04	Charming craftsman 3 bdm house	Cozy family craftman house in beautiful neighb...	Cozy family craftman house in beautiful neighb...	Cozy family craftman house in beautiful neighb...	none	We are in the beautiful neighborhood of Queen ...	Belltown	The nearest public transit bus (D Line) is 2 b...	NaN	NaN	https://a1.muscache.com/ac/pictures/6120468/b0...	NaN	1452570	https://www.airbnb.com/users/show/1452570	Emily	2011-11-29	Seattle, Washington, United States	Hi, I live in Seattle, Washington but I'm orig...	within an hour	100%	NaN	f	https://a0.muscache.com/ac/users/1452570/profi...	https://a0.muscache.com/ac/users/1452570/profi...	Queen Anne	2.0	2.0	['email', 'phone', 'facebook', 'reviews', 'kba']	t	t	14th Ave W, Seattle, WA 98119, United States	Queen Anne	West Queen Anne	Queen Anne	Seattle	WA	98119	Seattle	Seattle, WA	US	United States	47.632918	-122.372471	t	House	Entire home/apt	6	2.0	3.0	3.0	Real Bed	{TV,"Cable TV",Internet,"Wireless Internet",Ki...	NaN	$450.00	NaN	NaN	$700.00	$125.00	6	$15.00	1	1125	7 weeks ago	t	30	60	90	365	2016-01-04	38	2012-07-10	2015-10-24	92.0	9.0	9.0	10.0	10.0	9.0	9.0	f	NaN	WASHINGTON	f	strict	f	f	1	0.89

	id	listing_url	scrape_id	last_scraped	name	summary	space	description	experiences_offered	neighborhood_overview	notes	transit	thumbnail_url	medium_url	picture_url	xl_picture_url	host_id	host_url	host_name	host_since	host_location	host_about	host_response_time	host_response_rate	host_acceptance_rate	host_is_superhost	host_thumbnail_url	host_picture_url	host_neighbourhood	host_listings_count	host_total_listings_count	host_verifications	host_has_profile_pic	host_identity_verified	street	neighbourhood	neighbourhood_cleansed	neighbourhood_group_cleansed	city	state	zipcode	market	smart_location	country_code	country	latitude	longitude	is_location_exact	property_type	room_type	accommodates	bathrooms	bedrooms	beds	bed_type	amenities	square_feet	price	weekly_price	monthly_price	security_deposit	cleaning_fee	guests_included	extra_people	minimum_nights	maximum_nights	calendar_updated	has_availability	availability_30	availability_60	availability_90	availability_365	calendar_last_scraped	number_of_reviews	first_review	last_review	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	requires_license	license	jurisdiction_names	instant_bookable	cancellation_policy	require_guest_profile_picture	require_guest_phone_verification	calculated_host_listings_count	reviews_per_month
3813	8101950	https://www.airbnb.com/rooms/8101950	20160104002432	2016-01-04	3BR Mountain View House in Seattle	Our 3BR/2BA house boasts incredible views of t...	Our 3BR/2BA house bright, stylish, and wheelch...	Our 3BR/2BA house boasts incredible views of t...	none	We're located near lots of family fun. Woodlan...	NaN	NaN	https://a2.muscache.com/ac/pictures/103217071/...	https://a2.muscache.com/im/pictures/103217071/...	https://a2.muscache.com/ac/pictures/103217071/...	https://a2.muscache.com/ac/pictures/103217071/...	31148752	https://www.airbnb.com/users/show/31148752	Bo	2015-04-13	US	NaN	within a few hours	99%	100%	f	https://a2.muscache.com/ac/users/31148752/prof...	https://a2.muscache.com/ac/users/31148752/prof...	Holly	354.0	354.0	['email', 'phone', 'linkedin', 'reviews', 'jum...	t	t	Northwest 48th Street, Seattle, WA 98107, Unit...	Fremont	Fremont	Other neighborhoods	Seattle	WA	98107	Seattle	Seattle, WA	US	United States	47.664295	-122.359170	t	House	Entire home/apt	6	2.0	3.0	3.0	Real Bed	{TV,"Cable TV",Internet,"Wireless Internet","A...	NaN	$359.00	NaN	NaN	NaN	$230.00	1	$0.00	3	1125	today	t	18	32	32	32	2016-01-04	1	2015-09-27	2015-09-27	80.0	8.0	10.0	4.0	8.0	10.0	8.0	f	NaN	WASHINGTON	f	strict	f	f	8	0.3
3814	8902327	https://www.airbnb.com/rooms/8902327	20160104002432	2016-01-04	Portage Bay View!-One Bedroom Apt	800 square foot 1 bedroom basement apartment w...	This space has a great view of Portage Bay wit...	800 square foot 1 bedroom basement apartment w...	none	The neighborhood is a quiet oasis that is clos...	This is a basement apartment in a newer reside...	Uber and Car2go are good options in Seattle. T...	https://a2.muscache.com/ac/pictures/626d4b1f-6...	https://a2.muscache.com/im/pictures/626d4b1f-6...	https://a2.muscache.com/ac/pictures/626d4b1f-6...	https://a2.muscache.com/ac/pictures/626d4b1f-6...	46566046	https://www.airbnb.com/users/show/46566046	Glen	2015-10-14	Seattle, Washington, United States	I am a 58 year old male that is married to Mag...	within an hour	100%	100%	f	https://a2.muscache.com/ac/pictures/d7e59b0d-8...	https://a2.muscache.com/ac/pictures/d7e59b0d-8...	Portage Bay	1.0	1.0	['email', 'phone', 'facebook', 'reviews', 'jum...	t	t	Fuhrman Avenue East, Seattle, WA 98102, United...	Portage Bay	Portage Bay	Capitol Hill	Seattle	WA	98102	Seattle	Seattle, WA	US	United States	47.649552	-122.318309	t	Apartment	Entire home/apt	4	1.0	1.0	2.0	Real Bed	{TV,"Cable TV",Internet,"Wireless Internet",Ki...	NaN	$79.00	NaN	NaN	$500.00	$50.00	3	$25.00	2	29	2 days ago	t	6	26	44	273	2016-01-04	2	2015-12-18	2015-12-24	100.0	10.0	10.0	10.0	10.0	10.0	10.0	f	NaN	WASHINGTON	f	moderate	f	f	1	2.0
3815	10267360	https://www.airbnb.com/rooms/10267360	20160104002432	2016-01-04	Private apartment view of Lake WA	Very comfortable lower unit. Quiet, charming m...	NaN	Very comfortable lower unit. Quiet, charming m...	none	NaN	NaN	NaN	https://a2.muscache.com/ac/pictures/a5974f04-2...	https://a2.muscache.com/im/pictures/a5974f04-2...	https://a2.muscache.com/ac/pictures/a5974f04-2...	https://a2.muscache.com/ac/pictures/a5974f04-2...	52791370	https://www.airbnb.com/users/show/52791370	Virginia	2015-12-30	US	NaN	NaN	NaN	NaN	f	https://a2.muscache.com/ac/pictures/efc75826-1...	https://a2.muscache.com/ac/pictures/efc75826-1...	NaN	1.0	1.0	['phone']	t	f	South Laurel Street, Seattle, WA 98178, United...	NaN	Rainier Beach	Rainier Valley	Seattle	WA	98178	Seattle	Seattle, WA	US	United States	47.508453	-122.240607	f	House	Entire home/apt	2	1.0	1.0	1.0	Real Bed	{"Cable TV","Wireless Internet",Kitchen,"Free ...	NaN	$93.00	$450.00	NaN	$250.00	$35.00	2	$20.00	1	7	4 days ago	t	29	59	88	88	2016-01-04	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	f	NaN	WASHINGTON	f	moderate	f	f	1	NaN
3816	9604740	https://www.airbnb.com/rooms/9604740	20160104002432	2016-01-04	Amazing View with Modern Comfort!	Cozy studio condo in the heart on Madison Park...	Fully furnished unit to accommodate most needs...	Cozy studio condo in the heart on Madison Park...	none	Madison Park offers a peaceful slow pace upsca...	NaN	Yes	https://a2.muscache.com/ac/pictures/202e4ad6-b...	https://a2.muscache.com/im/pictures/202e4ad6-b...	https://a2.muscache.com/ac/pictures/202e4ad6-b...	https://a2.muscache.com/ac/pictures/202e4ad6-b...	25522052	https://www.airbnb.com/users/show/25522052	Karen	2015-01-03	Tacoma, Washington, United States	NaN	within an hour	100%	NaN	f	https://a0.muscache.com/ac/users/25522052/prof...	https://a0.muscache.com/ac/users/25522052/prof...	NaN	1.0	1.0	['email', 'phone', 'facebook', 'reviews', 'kba']	t	t	43rd Avenue East, Seattle, WA 98112, United St...	NaN	Madison Park	Capitol Hill	Seattle	WA	98112	Seattle	Seattle, WA	US	United States	47.632335	-122.275530	f	Condominium	Entire home/apt	2	1.0	0.0	1.0	Real Bed	{TV,"Wireless Internet",Kitchen,"Free Parking ...	NaN	$99.00	NaN	NaN	$300.00	$45.00	1	$0.00	3	1125	never	t	30	60	90	179	2016-01-04	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	f	NaN	WASHINGTON	f	moderate	f	f	1	NaN
3817	10208623	https://www.airbnb.com/rooms/10208623	20160104002432	2016-01-04	Large Lakefront Apartment	All hardwood floors, fireplace, 65" TV with Xb...	NaN	All hardwood floors, fireplace, 65" TV with Xb...	none	NaN	Also our puppy will be boarded away.	NaN	https://a2.muscache.com/ac/pictures/596705b3-0...	https://a2.muscache.com/im/pictures/596705b3-0...	https://a2.muscache.com/ac/pictures/596705b3-0...	https://a2.muscache.com/ac/pictures/596705b3-0...	14703116	https://www.airbnb.com/users/show/14703116	Gil	2014-04-25	Seattle, Washington, United States	NaN	within a day	100%	NaN	f	https://a2.muscache.com/ac/pictures/8864f979-b...	https://a2.muscache.com/ac/pictures/8864f979-b...	Queen Anne	1.0	1.0	['email', 'phone', 'reviews', 'kba']	t	t	Westlake Avenue North, Seattle, WA 98109, Unit...	Queen Anne	East Queen Anne	Queen Anne	Seattle	WA	98109	Seattle	Seattle, WA	US	United States	47.641186	-122.342085	t	Apartment	Entire home/apt	3	1.5	2.0	1.0	Real Bed	{TV,"Cable TV",Internet,"Wireless Internet",Ki...	NaN	$87.00	NaN	NaN	NaN	NaN	1	$0.00	1	1125	a week ago	t	7	7	7	7	2016-01-04	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	f	NaN	WASHINGTON	f	flexible	f	f	1	NaN

	listing_id	id	date	reviewer_id	reviewer_name	comments
0	7202016	38917982	2015-07-19	28943674	Bianca	Cute and cozy place. Perfect location to every...
1	7202016	39087409	2015-07-20	32440555	Frank	Kelly has a great room in a very central locat...
2	7202016	39820030	2015-07-26	37722850	Ian	Very spacious apartment, and in a great neighb...
3	7202016	40813543	2015-08-02	33671805	George	Close to Seattle Center and all it has to offe...
4	7202016	41986501	2015-08-10	34959538	Ming	Kelly was a great host and very accommodating ...

	listing_id	date	available	price
0	241032	2016-01-04	t	$85.00
1	241032	2016-01-05	t	$85.00
2	241032	2016-01-06	f	NaN
3	241032	2016-01-07	f	NaN
4	241032	2016-01-08	f	NaN

	id	scrape_id	host_id	host_listings_count	host_total_listings_count	latitude	longitude	accommodates	bathrooms	bedrooms	beds	square_feet	guests_included	minimum_nights	maximum_nights	availability_30	availability_60	availability_90	availability_365	number_of_reviews	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	license	calculated_host_listings_count	reviews_per_month
count	3.818000e+03	3.818000e+03	3.818000e+03	3816.000000	3816.000000	3818.000000	3818.000000	3818.000000	3802.000000	3812.000000	3817.000000	97.000000	3818.000000	3818.000000	3818.000000	3818.000000	3818.000000	3818.000000	3818.000000	3818.000000	3171.000000	3160.000000	3165.000000	3160.000000	3167.000000	3163.000000	3162.000000	0.0	3818.000000	3191.000000
mean	5.550111e+06	2.016010e+13	1.578556e+07	7.157757	7.157757	47.628961	-122.333103	3.349398	1.259469	1.307712	1.735394	854.618557	1.672603	2.369303	780.447617	16.786276	36.814825	58.082504	244.772656	22.223415	94.539262	9.636392	9.556398	9.786709	9.809599	9.608916	9.452245	NaN	2.946307	2.078919
std	2.962660e+06	0.000000e+00	1.458382e+07	28.628149	28.628149	0.043052	0.031745	1.977599	0.590369	0.883395	1.139480	671.404893	1.311040	16.305902	1683.589007	12.173637	23.337541	34.063845	126.772526	37.730892	6.606083	0.698031	0.797274	0.595499	0.568211	0.629053	0.750259	NaN	5.893029	1.822348
min	3.335000e+03	2.016010e+13	4.193000e+03	1.000000	1.000000	47.505088	-122.417219	1.000000	0.000000	0.000000	1.000000	0.000000	0.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	20.000000	2.000000	3.000000	2.000000	2.000000	4.000000	2.000000	NaN	1.000000	0.020000
25%	3.258256e+06	2.016010e+13	3.275204e+06	1.000000	1.000000	47.609418	-122.354320	2.000000	1.000000	1.000000	1.000000	420.000000	1.000000	1.000000	60.000000	2.000000	13.000000	28.000000	124.000000	2.000000	93.000000	9.000000	9.000000	10.000000	10.000000	9.000000	9.000000	NaN	1.000000	0.695000
50%	6.118244e+06	2.016010e+13	1.055814e+07	1.000000	1.000000	47.623601	-122.328874	3.000000	1.000000	1.000000	1.000000	750.000000	1.000000	2.000000	1125.000000	20.000000	46.000000	73.000000	308.000000	9.000000	96.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	NaN	1.000000	1.540000
75%	8.035127e+06	2.016010e+13	2.590309e+07	3.000000	3.000000	47.662694	-122.310800	4.000000	1.000000	2.000000	2.000000	1200.000000	2.000000	2.000000	1125.000000	30.000000	59.000000	89.000000	360.000000	26.000000	99.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	NaN	2.000000	3.000000
max	1.034016e+07	2.016010e+13	5.320861e+07	502.000000	502.000000	47.733358	-122.240607	16.000000	8.000000	7.000000	15.000000	3000.000000	15.000000	1000.000000	100000.000000	30.000000	60.000000	90.000000	365.000000	474.000000	100.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	NaN	37.000000	12.150000

	listing_id	id	reviewer_id
count	8.484900e+04	8.484900e+04	8.484900e+04
mean	3.005067e+06	3.058765e+07	1.701301e+07
std	2.472877e+06	1.636613e+07	1.353704e+07
min	4.291000e+03	3.721000e+03	1.500000e+01
25%	7.946330e+05	1.725127e+07	5.053141e+06
50%	2.488228e+06	3.228809e+07	1.413476e+07
75%	4.694479e+06	4.457648e+07	2.762402e+07
max	1.024814e+07	5.873651e+07	5.281274e+07

	listing_id
count	1.393570e+06
mean	5.550111e+06
std	2.962274e+06
min	3.335000e+03
25%	3.258213e+06
50%	6.118244e+06
75%	8.035212e+06
max	1.034016e+07

	id	host_id	host_response_rate	host_is_superhost	host_total_listings_count	host_has_profile_pic	host_identity_verified	neighbourhood_cleansed	property_type	accommodates	bathrooms	bedrooms	beds	bed_type	amenities	price	guests_included	extra_people	minimum_nights	maximum_nights	availability_30	availability_60	availability_90	availability_365	number_of_reviews	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	cancellation_policy	require_guest_profile_picture	require_guest_phone_verification	calculated_host_listings_count	reviews_per_month
0	127	229	96.0	1	3.0	2	2	81	1	4	1.0	1.0	1.0	4	1467	85.0	2	5.0	1	365	14	41	71	346	207	95.0	10.0	10.0	10.0	10.0	9.0	10.0	1	0	0	2	4.07
1	374	753	98.0	2	6.0	2	2	81	1	4	1.0	1.0	1.0	4	2729	150.0	1	0.0	2	90	13	13	16	291	43	96.0	10.0	10.0	10.0	10.0	10.0	10.0	2	1	1	6	1.48
2	977	1534	67.0	1	2.0	2	2	81	10	11	4.5	5.0	7.0	4	1350	975.0	10	25.0	4	30	1	6	17	220	20	97.0	10.0	10.0	10.0	10.0	10.0	10.0	2	0	0	2	1.15
3	2512	1162	NaN	1	1.0	2	2	81	1	3	1.0	0.0	2.0	4	864	100.0	1	0.0	1	1125	0	0	0	143	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0	1	0.00
4	142	319	100.0	1	2.0	2	2	81	10	6	2.0	3.0	3.0	4	2060	450.0	6	15.0	1	1125	30	60	90	365	38	92.0	9.0	9.0	10.0	10.0	9.0	9.0	2	0	0	1	0.89

	id	host_id	host_response_rate	host_is_superhost	host_total_listings_count	host_has_profile_pic	host_identity_verified	neighbourhood_cleansed	property_type	room_type	accommodates	bathrooms	bedrooms	beds	bed_type	amenities	price	guests_included	extra_people	minimum_nights	maximum_nights	availability_30	availability_60	availability_90	availability_365	number_of_reviews	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	instant_bookable	cancellation_policy	require_guest_profile_picture	require_guest_phone_verification	calculated_host_listings_count	reviews_per_month
id	1.000000	0.526779	-0.023486	-0.179286	-0.017558	-0.031746	-0.165083	-0.000194	-0.044046	0.012270	-0.072106	-0.045030	-0.057565	-0.048582	-0.015800	0.089404	-0.051899	-0.099935	-0.088781	-0.023612	0.096889	-0.063821	-0.072278	-0.084045	-0.159727	-0.491456	-0.419528	-0.422457	-0.413166	-0.431296	-0.427130	-0.415672	-0.413519	0.042352	-0.277061	-0.215819	-0.256966	-0.054340	-0.119738
host_id	0.526779	1.000000	-0.017555	-0.091425	-0.074499	-0.021404	-0.232388	0.007764	-0.043996	0.036661	-0.107626	-0.043565	-0.079650	-0.075468	-0.037162	0.062870	-0.050778	-0.082703	-0.033159	-0.022948	0.061492	-0.040036	-0.044780	-0.047325	-0.089550	-0.264044	-0.170898	-0.171969	-0.166864	-0.176127	-0.172379	-0.166775	-0.163151	0.082946	-0.245062	-0.299143	-0.334560	-0.212590	0.027363
host_response_rate	-0.023486	-0.017555	1.000000	0.153810	0.022147	-0.000372	0.090387	-0.031925	0.044479	-0.010282	0.005433	0.005279	-0.001040	0.015617	0.037637	0.007689	-0.014071	0.065056	0.019094	0.006604	-0.082118	-0.047150	-0.035688	-0.031227	-0.087838	0.104779	0.097171	0.090600	0.102826	0.090744	0.093257	0.080213	0.096658	0.094017	0.092686	0.015841	0.002301	-0.087481	0.168230
host_is_superhost	-0.179286	-0.091425	0.153810	1.000000	-0.039775	0.054250	0.151422	-0.005766	0.033836	-0.015971	-0.002012	0.001362	-0.026557	-0.022382	0.028015	0.032379	0.012868	0.060904	0.041727	-0.005713	-0.037321	0.013424	0.031909	0.042953	-0.012394	0.262108	0.220262	0.219492	0.225617	0.207942	0.204925	0.202671	0.222906	0.082293	0.110963	0.099962	0.104161	-0.092745	0.311759
host_total_listings_count	-0.017558	-0.074499	0.022147	-0.039775	1.000000	0.010480	0.086930	-0.101094	-0.111521	-0.057998	0.111284	0.066845	0.062644	0.085609	0.031055	-0.023497	0.095591	-0.059217	-0.063490	0.001901	0.022699	0.119754	0.124721	0.124040	0.086184	-0.062136	-0.031702	-0.032574	-0.022896	-0.037898	-0.031785	-0.012787	-0.033066	-0.044281	0.206622	0.103811	0.090513	0.224256	-0.093774
host_has_profile_pic	-0.031746	-0.021404	-0.000372	0.054250	0.010480	1.000000	0.128073	0.014681	0.026393	0.000125	0.012362	0.009117	0.020640	0.014993	-0.008678	-0.014043	-0.021250	0.014036	-0.008958	0.001299	0.001401	-0.021428	-0.015065	-0.013153	0.029354	0.023114	0.043570	0.044987	0.043464	0.046023	0.045485	0.043987	0.042760	-0.003438	0.014179	0.013941	0.015186	0.015199	0.030048
host_identity_verified	-0.165083	-0.232388	0.090387	0.151422	0.086930	0.128073	1.000000	-0.029047	0.008028	-0.015848	0.041925	-0.009932	-0.005346	0.014965	0.021763	0.022243	0.002895	0.033691	0.046204	-0.027499	-0.012315	0.022072	0.031827	0.044146	0.024235	0.098041	0.199805	0.197649	0.200272	0.195021	0.195122	0.192436	0.192678	-0.001063	0.138001	0.112566	0.131929	0.059250	0.134237
neighbourhood_cleansed	-0.000194	0.007764	-0.031925	-0.005766	-0.101094	0.014681	-0.029047	1.000000	0.193953	0.112103	-0.020001	0.038187	0.063422	0.020385	-0.029527	-0.065899	-0.035083	0.056994	0.028576	-0.016271	-0.027223	0.023969	0.018351	0.018572	0.048532	0.001414	-0.000099	0.002624	-0.004881	-0.001239	-0.001074	-0.009411	0.006711	-0.027020	-0.056267	-0.094926	-0.094076	0.061097	-0.016853
property_type	-0.044046	-0.043996	0.044479	0.033836	-0.111521	0.026393	0.008028	0.193953	1.000000	0.274013	0.119473	0.240772	0.285680	0.163768	0.036114	-0.071746	0.043210	0.145762	0.053217	-0.017254	-0.031952	0.025113	0.027885	0.029139	0.037427	0.009201	-0.002173	-0.006709	-0.008000	-0.007489	-0.009368	-0.029319	0.004783	-0.057887	-0.033126	-0.062758	-0.056390	0.000627	-0.021588
room_type	0.012270	0.036661	-0.010282	-0.015971	-0.057998	0.000125	-0.015848	0.112103	0.274013	1.000000	-0.460971	-0.099129	-0.233161	-0.344106	-0.208938	-0.140851	-0.433745	-0.250081	-0.083386	-0.025187	-0.005987	0.152464	0.163441	0.166518	0.133112	0.037427	-0.024999	-0.033530	-0.042728	-0.030754	-0.028045	-0.036787	-0.019521	-0.071934	-0.206957	-0.045679	-0.060012	0.165843	0.032835
accommodates	-0.072106	-0.107626	0.005433	-0.002012	0.111284	0.012362	0.041925	-0.020001	0.119473	-0.460971	1.000000	0.533586	0.769680	0.860714	0.130372	0.101470	0.659512	0.532796	0.148390	0.017097	0.003291	-0.043169	-0.048761	-0.060468	-0.031535	-0.072978	0.036648	0.034294	0.042512	0.039341	0.038589	0.035531	0.029379	0.024355	0.282552	0.060069	0.064525	-0.029525	-0.101904
bathrooms	-0.045030	-0.043565	0.005279	0.001362	0.066845	0.009117	-0.009932	0.038187	0.240772	-0.099129	0.533586	1.000000	0.605989	0.528864	0.068096	0.041466	0.519623	0.304501	0.080811	0.006567	-0.013941	-0.044530	-0.054642	-0.062456	-0.006181	-0.094550	0.007483	0.000434	0.004769	0.001875	-0.000289	0.000106	0.004277	-0.048376	0.137011	0.020483	0.010624	-0.004103	-0.134198
bedrooms	-0.057565	-0.079650	-0.001040	-0.026557	0.062644	0.020640	-0.005346	0.063422	0.285680	-0.233161	0.769680	0.605989	1.000000	0.752720	0.080169	0.070969	0.632741	0.456755	0.109494	0.012084	-0.008061	-0.078166	-0.091714	-0.104119	-0.051314	-0.106732	-0.010024	-0.014239	-0.013390	-0.011083	-0.012474	-0.017355	-0.013831	-0.070145	0.193780	0.009484	0.008340	-0.045418	-0.191424
beds	-0.048582	-0.075468	0.015617	-0.022382	0.085609	0.014993	0.014965	0.020385	0.163768	-0.344106	0.860714	0.528864	0.752720	1.000000	0.104693	0.046418	0.595196	0.460561	0.131039	0.002703	-0.009192	-0.028991	-0.036814	-0.047924	-0.010147	-0.088811	0.014087	0.008172	0.016969	0.013675	0.014291	0.012470	0.010581	0.024561	0.239074	0.036514	0.045101	0.010597	-0.119031
bed_type	-0.015800	-0.037162	0.037637	0.028015	0.031055	-0.008678	0.021763	-0.029527	0.036114	-0.208938	0.130372	0.068096	0.080169	0.104693	1.000000	0.019528	0.116214	0.051333	0.004903	0.008635	-0.000616	-0.001771	-0.000551	0.003371	0.007070	0.020279	0.034034	0.034728	0.036399	0.033097	0.031358	0.034804	0.032086	0.044118	0.122112	0.033292	0.040005	0.048443	0.024768
amenities	0.089404	0.062870	0.007689	0.032379	-0.023497	-0.014043	0.022243	-0.065899	-0.071746	-0.140851	0.101470	0.041466	0.070969	0.046418	0.019528	1.000000	0.078647	0.081836	0.043266	-0.013900	0.029876	-0.059684	-0.062954	-0.064805	-0.082438	-0.037006	-0.029342	-0.029243	-0.029012	-0.030630	-0.031560	-0.026889	-0.030823	0.019906	0.001967	-0.022802	-0.025079	-0.134355	0.017130
price	-0.051899	-0.050778	-0.014071	0.012868	0.095591	-0.021250	0.002895	-0.035083	0.043210	-0.433745	0.659512	0.519623	0.632741	0.595196	0.116214	0.078647	1.000000	0.399100	0.131117	0.017728	-0.003902	-0.039432	-0.051732	-0.061422	-0.018035	-0.124812	-0.021718	-0.023546	-0.018653	-0.025696	-0.026680	-0.014504	-0.034318	-0.031011	0.215375	0.064863	0.059163	-0.053920	-0.190477
guests_included	-0.099935	-0.082703	0.065056	0.060904	-0.059217	0.014036	0.033691	0.056994	0.145762	-0.250081	0.532796	0.304501	0.456755	0.460561	0.051333	0.081836	0.399100	1.000000	0.422452	-0.001659	-0.018637	-0.045357	-0.046944	-0.047100	-0.048922	0.028114	0.059208	0.060765	0.063163	0.065148	0.061907	0.056200	0.056260	0.015738	0.216053	0.003668	0.013275	-0.077759	0.003972
extra_people	-0.088781	-0.033159	0.019094	0.041727	-0.063490	-0.008958	0.046204	0.028576	0.053217	-0.083386	0.148390	0.080811	0.109494	0.131039	0.004903	0.043266	0.131117	0.422452	1.000000	-0.010692	-0.018509	0.026570	0.021808	0.026427	0.012101	0.044395	0.078921	0.078441	0.084772	0.074547	0.075735	0.083744	0.075668	0.022009	0.142026	0.024782	0.021103	-0.043115	0.040026
minimum_nights	-0.023612	-0.022948	0.006604	-0.005713	0.001901	0.001299	-0.027499	-0.016271	-0.017254	-0.025187	0.017097	0.006567	0.012084	0.002703	0.008635	-0.013900	0.017728	-0.001659	-0.010692	1.000000	0.003161	0.013205	0.010290	0.009076	0.009087	-0.013818	0.003776	0.001580	-0.002556	0.000795	0.004618	0.006118	0.002183	-0.014624	0.017332	0.004831	0.001485	0.000512	-0.029727
maximum_nights	0.096889	0.061492	-0.082118	-0.037321	0.022699	0.001401	-0.012315	-0.027223	-0.031952	-0.005987	0.003291	-0.013941	-0.008061	-0.009192	-0.000616	0.029876	-0.003902	-0.018637	-0.018509	0.003161	1.000000	0.012254	-0.009236	-0.002945	0.007265	-0.081578	-0.039596	-0.035457	-0.036216	-0.043953	-0.039785	-0.037799	-0.038967	-0.013534	-0.015909	-0.028365	-0.035810	0.031274	-0.043766
availability_30	-0.063821	-0.040036	-0.047150	0.013424	0.119754	-0.021428	0.022072	0.023969	0.025113	0.152464	-0.043169	-0.044530	-0.078166	-0.028991	-0.001771	-0.059684	-0.039432	-0.045357	0.026570	0.013205	0.012254	1.000000	0.936122	0.875778	0.503881	0.074611	0.024553	0.024417	0.034499	0.028055	0.029290	0.028067	0.019250	0.041661	0.063477	0.070533	0.069565	0.124677	0.057327
availability_60	-0.072278	-0.044780	-0.035688	0.031909	0.124721	-0.015065	0.031827	0.018351	0.027885	0.163441	-0.048761	-0.054642	-0.091714	-0.036814	-0.000551	-0.062954	-0.051732	-0.046944	0.021808	0.010290	-0.009236	0.936122	1.000000	0.973353	0.572857	0.099099	0.050191	0.051953	0.061069	0.055348	0.054545	0.052917	0.046599	0.065150	0.076134	0.070722	0.072845	0.130154	0.111184
availability_90	-0.084045	-0.047325	-0.031227	0.042953	0.124040	-0.013153	0.044146	0.018572	0.029139	0.166518	-0.060468	-0.062456	-0.104119	-0.047924	0.003371	-0.064805	-0.061422	-0.047100	0.026427	0.009076	-0.002945	0.875778	0.973353	1.000000	0.619355	0.105257	0.070979	0.073816	0.081156	0.075963	0.073861	0.072132	0.066886	0.073906	0.084865	0.066495	0.072351	0.132391	0.131875
availability_365	-0.159727	-0.089550	-0.087838	-0.012394	0.086184	0.029354	0.024235	0.048532	0.037427	0.133112	-0.031535	-0.006181	-0.051314	-0.010147	0.007070	-0.082438	-0.018035	-0.048922	0.012101	0.009087	0.007265	0.503881	0.572857	0.619355	1.000000	0.094273	0.069420	0.072251	0.070895	0.075378	0.072752	0.072679	0.066784	0.003744	0.047364	0.015704	0.037126	0.136881	0.034873
number_of_reviews	-0.491456	-0.264044	0.104779	0.262108	-0.062136	0.023114	0.098041	0.001414	0.009201	0.037427	-0.072978	-0.094550	-0.106732	-0.088811	0.020279	-0.037006	-0.124812	0.028114	0.044395	-0.013818	-0.081578	0.074611	0.099099	0.105257	0.094273	1.000000	0.267817	0.275221	0.272397	0.277842	0.273798	0.262834	0.270940	0.123071	0.118048	0.186719	0.213455	-0.067194	0.601509
review_scores_rating	-0.419528	-0.170898	0.097171	0.220262	-0.031702	0.043570	0.199805	-0.000099	-0.002173	-0.024999	0.036648	0.007483	-0.010024	0.014087	0.034034	-0.029342	-0.021718	0.059208	0.078921	0.003776	-0.039596	0.024553	0.050191	0.070979	0.069420	0.267817	1.000000	0.979406	0.982442	0.978234	0.985243	0.976161	0.981976	0.094494	0.209938	0.069252	0.084511	-0.017523	0.432112
review_scores_accuracy	-0.422457	-0.171969	0.090600	0.219492	-0.032574	0.044987	0.197649	0.002624	-0.006709	-0.033530	0.034294	0.000434	-0.014239	0.008172	0.034728	-0.029243	-0.023546	0.060765	0.078441	0.001580	-0.035457	0.024417	0.051953	0.073816	0.072251	0.275221	0.979406	1.000000	0.979645	0.983324	0.980084	0.977689	0.982167	0.097687	0.211261	0.066378	0.081978	-0.009938	0.442096
review_scores_cleanliness	-0.413166	-0.166864	0.102826	0.225617	-0.022896	0.043464	0.200272	-0.004881	-0.008000	-0.042728	0.042512	0.004769	-0.013390	0.016969	0.036399	-0.029012	-0.018653	0.063163	0.084772	-0.002556	-0.036216	0.034499	0.061069	0.081156	0.070895	0.272397	0.982442	0.979645	1.000000	0.976824	0.979553	0.971349	0.976158	0.103391	0.220859	0.075094	0.088532	-0.013417	0.441598
review_scores_checkin	-0.431296	-0.176127	0.090744	0.207942	-0.037898	0.046023	0.195021	-0.001239	-0.007489	-0.030754	0.039341	0.001875	-0.011083	0.013675	0.033097	-0.030630	-0.025696	0.065148	0.074547	0.000795	-0.043953	0.028055	0.055348	0.075963	0.075378	0.277842	0.978234	0.983324	0.976824	1.000000	0.988187	0.979843	0.979225	0.097087	0.207792	0.070967	0.085630	-0.009513	0.437739
review_scores_communication	-0.427130	-0.172379	0.093257	0.204925	-0.031785	0.045485	0.195122	-0.001074	-0.009368	-0.028045	0.038589	-0.000289	-0.012474	0.014291	0.031358	-0.031560	-0.026680	0.061907	0.075735	0.004618	-0.039785	0.029290	0.054545	0.073861	0.072752	0.273798	0.985243	0.980084	0.979553	0.988187	1.000000	0.981594	0.980996	0.096809	0.210278	0.072468	0.087086	-0.007803	0.434476
review_scores_location	-0.415672	-0.166775	0.080213	0.202671	-0.012787	0.043987	0.192436	-0.009411	-0.029319	-0.036787	0.035531	0.000106	-0.017355	0.012470	0.034804	-0.026889	-0.014504	0.056200	0.083744	0.006118	-0.037799	0.028067	0.052917	0.072132	0.072679	0.262834	0.976161	0.977689	0.971349	0.979843	0.981594	1.000000	0.979965	0.095636	0.213780	0.065805	0.081308	-0.002813	0.428736
review_scores_value	-0.413519	-0.163151	0.096658	0.222906	-0.033066	0.042760	0.192678	0.006711	0.004783	-0.019521	0.029379	0.004277	-0.013831	0.010581	0.032086	-0.030823	-0.034318	0.056260	0.075668	0.002183	-0.038967	0.019250	0.046599	0.066886	0.066784	0.270940	0.981976	0.982167	0.976158	0.979225	0.980996	0.979965	1.000000	0.100836	0.205219	0.061137	0.075739	-0.017843	0.439302
instant_bookable	0.042352	0.082946	0.094017	0.082293	-0.044281	-0.003438	-0.001063	-0.027020	-0.057887	-0.071934	0.024355	-0.048376	-0.070145	0.024561	0.044118	0.019906	-0.031011	0.015738	0.022009	-0.014624	-0.013534	0.041661	0.065150	0.073906	0.003744	0.123071	0.094494	0.097687	0.103391	0.097087	0.096809	0.095636	0.100836	1.000000	0.051115	-0.014844	-0.012281	0.006972	0.272128
cancellation_policy	-0.277061	-0.245062	0.092686	0.110963	0.206622	0.014179	0.138001	-0.056267	-0.033126	-0.206957	0.282552	0.137011	0.193780	0.239074	0.122112	0.001967	0.215375	0.216053	0.142026	0.017332	-0.015909	0.063477	0.076134	0.084865	0.047364	0.118048	0.209938	0.211261	0.220859	0.207792	0.210278	0.213780	0.205219	0.051115	1.000000	0.210900	0.220589	0.215044	0.071186
require_guest_profile_picture	-0.215819	-0.299143	0.015841	0.099962	0.103811	0.013941	0.112566	-0.094926	-0.062758	-0.045679	0.060069	0.020483	0.009484	0.036514	0.033292	-0.022802	0.064863	0.003668	0.024782	0.004831	-0.028365	0.070533	0.070722	0.066495	0.015704	0.186719	0.069252	0.066378	0.075094	0.070967	0.072468	0.065805	0.061137	-0.014844	0.210900	1.000000	0.873632	0.203791	0.015392
require_guest_phone_verification	-0.256966	-0.334560	0.002301	0.104161	0.090513	0.015186	0.131929	-0.094076	-0.056390	-0.060012	0.064525	0.010624	0.008340	0.045101	0.040005	-0.025079	0.059163	0.013275	0.021103	0.001485	-0.035810	0.069565	0.072845	0.072351	0.037126	0.213455	0.084511	0.081978	0.088532	0.085630	0.087086	0.081308	0.075739	-0.012281	0.220589	0.873632	1.000000	0.180596	0.011208
calculated_host_listings_count	-0.054340	-0.212590	-0.087481	-0.092745	0.224256	0.015199	0.059250	0.061097	0.000627	0.165843	-0.029525	-0.004103	-0.045418	0.010597	0.048443	-0.134355	-0.053920	-0.077759	-0.043115	0.000512	0.031274	0.124677	0.130154	0.132391	0.136881	-0.067194	-0.017523	-0.009938	-0.013417	-0.009513	-0.007803	-0.002813	-0.017843	0.006972	0.215044	0.203791	0.180596	1.000000	-0.079649
reviews_per_month	-0.119738	0.027363	0.168230	0.311759	-0.093774	0.030048	0.134237	-0.016853	-0.021588	0.032835	-0.101904	-0.134198	-0.191424	-0.119031	0.024768	0.017130	-0.190477	0.003972	0.040026	-0.029727	-0.043766	0.057327	0.111184	0.131875	0.034873	0.601509	0.432112	0.442096	0.441598	0.437739	0.434476	0.428736	0.439302	0.272128	0.071186	0.015392	0.011208	-0.079649	1.000000